The economics of corruption perceptions: structure vs change, 2012-2024

How economic development shapes corruption perceptions, yet dramatic improvements remain possible

A comprehensive analysis of global corruption perceptions revealing how economic development strongly predicts governance quality, while individual countries can still achieve dramatic improvements. Using World Bank income classifications and 13 years of Transparency International data, this Makeover Monday project transforms a static table into an engaging dual-chart story showing both structural patterns and exceptional cases of anti-corruption progress across 180+ countries.

Author

Steven Ponce

Published

August 5, 2025

Original

The original visualization Corruption Perceptions Index (CPI) comes from Our orld in Data

Makeover

Figure 1: Two-panel chart showing corruption perceptions by income level. Left panel: beeswarm plot reveals higher-income countries (orange dots) cluster at better corruption scores (60-90), while lower-income countries (blue dots) spread across worse scores (10-60). Right panel: The dumbbell chart shows the largest changes from 2012 to 2024, with Seychelles leading at a +20-point improvement and Estonia at a +12-point improvement, demonstrating that countries across all income levels can achieve dramatic anti-corruption progress.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
tidyverse,      # Easily Install and Load the 'Tidyverse'
  ggtext,         # Improved Text Rendering Support for 'ggplot2'
  showtext,       # Using Fonts More Easily in R Graphs
  scales,         # Scale Functions for Visualization
  glue,           # Interpreted String Literals
  ggbeeswarm,     # Categorical Scatter (Violin Point) Plots
  patchwork       # The Composer of Plots
  )
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  12,
    height =  10,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|
cpi_raw <- readxl::read_excel(
  here::here('data/MakeoverMonday/2025/Corruption Perception Index.xlsx')) |> 
  janitor::clean_names()
```

3. Examine the Data

Show code

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(cpi_raw)
skimr::skim(cpi_raw)
```

4. Tidy Data

Show code

```{r}
#| label: tidy
#| warning: false

# Data cleaning and preparation
cpi_clean <- cpi_raw |>
  filter(!is.na(corruption_perceptions_index)) |>
  filter(year >= 2012, year <= 2024)

# Helper function to assign World Bank income groups (FY2024 classification)
create_income_groups <- function(data) {
  data |>
    mutate(
      income_group = case_when(
        # ---- High Income Countries (based on WB FY2024) ----
        code %in% c(
          # Western Europe & North America
          "DNK", "NOR", "SWE", "CHE", "SGP", "NZL", "FIN", "DEU", "NLD",
          "LUX", "AUT", "AUS", "CAN", "USA", "GBR", "BEL", "JPN", "FRA",
          "IRL", "ISL", "ESP", "ITA", "KOR", "ISR", 
          # Eastern Europe (High Income)
          "SVN", "CZE", "EST", "SVK", "PRT", "POL", "LTU", "LVA", "HUN", 
          "HRV", "GRC", "CYP", "MLT",
          # Other High Income
          "URY", "CHL", "PAN", "ARE", "QAT", "KWT", "BHR", "OMN",
          "SAU", "BRB", "TTO"
        ) ~ "High\nIncome",
        
        # ---- Upper Middle Income ----
        code %in% c(
          # Latin America
          "ARG", "MYS", "THA", "MEX", "CRI", "BRA", "COL", "PER", "ECU", "DOM",
          # Europe & Central Asia
          "ROU", "BGR", "TUR", "MNE", "SRB", "MKD", "ALB", "BIH", "ARM", "GEO", 
          "AZE", "KAZ", "BLR",
          # Other Upper Middle
          "CHN", "RUS", "ZAF", "BWA", "NAM", "JAM", "LBN", "JOR", "TUN", 
          "DZA", "IRQ", "IRN", "LBY", "GAB"
        ) ~ "Upper Middle\nIncome",
        
        # ---- Lower Middle Income ----
        code %in% c(
          # South & East Asia
          "IND", "IDN", "PHL", "VNM", "BGD", "PAK", "LKA", "NPL", "KHM",
          "LAO", "MMR", "MNG",
          # Europe & Central Asia
          "UZB", "KGZ", "TJK", "UKR", "MDA", 
          # Middle East & North Africa
          "MAR", "EGY", "PSE", "SYR", "YEM", "SDN", "DJI",
          # Latin America
          "BOL", "PRY", "GTM", "HND", "NIC", "SLV", "GUY", "SUR",
          # Sub-Saharan Africa
          "KEN", "TZA", "UGA", "RWA", "ZMB", "AGO", "CIV", "GHA", 
          "CMR", "SEN", "NGA", "BEN"
        ) ~ "Lower Middle\nIncome",
        
        # ---- Default: Low Income ----
        TRUE ~ "Low\nIncome"
      ),
      # Create ordered factor for logical display
      income_group = factor(
        income_group,
        levels = c("Low\nIncome", "Lower Middle\nIncome", 
                   "Upper Middle\nIncome", "High\nIncome"),
        ordered = TRUE
      )
    )
}

# Apply income classification
cpi_with_income <- cpi_clean |>
  create_income_groups()

# Calculate country trends for dumbbell chart
country_trends <- cpi_with_income |>
  group_by(entity) |>
  filter(any(year == 2012) & any(year == 2024)) |>
  summarise(
    trend = corruption_perceptions_index[year == 2024][1] - corruption_perceptions_index[year == 2012][1],
    latest_score = corruption_perceptions_index[year == 2024][1],
    earliest_score = corruption_perceptions_index[year == 2012][1],
    income_group = first(income_group),
    .groups = "drop"
  ) |>
  filter(!is.na(trend), !is.na(latest_score), !is.na(earliest_score))

# Beeswarm plot data
latest_data_income <- cpi_with_income |>
  filter(year == 2024) |>
  arrange(desc(corruption_perceptions_index))

# Dumbbell plot data
biggest_movers <- country_trends |>
  filter(abs(trend) >= 5) |>
  arrange(desc(trend)) |>
  slice_head(n = 20) |>
  mutate(
    income_abbrev = case_when(
      income_group == "High\nIncome" ~ "HI",
      income_group == "Upper Middle\nIncome" ~ "UMI",
      income_group == "Lower Middle\nIncome" ~ "LMI",
      income_group == "Low\nIncome" ~ "LI",
      TRUE ~ "Unknown"
    ),
    entity_with_income = glue("{entity} ({income_abbrev})"),
    entity_short = if_else(
      nchar(entity_with_income) > 25,
      glue("{str_sub(entity, 1, 18)}... ({income_abbrev})"),
      entity_with_income
    ),
    entity_short = fct_reorder(entity_short, latest_score)
  )
```

5. Visualization Parameters

Show code

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = list(
  "Low\nIncome" = "#2166AC",
  "Lower Middle\nIncome" = "#5AAE61",
  "Upper Middle\nIncome" = "#9970AB",
  "High\nIncome" = "#E08214"
))

### |-  titles and caption ----
title_text <- str_glue("The economics of corruption perceptions: structure vs change, 2012-2024")

subtitle_text <- str_glue(
  "How economic development shapes corruption perceptions, yet dramatic improvements remain possible"
)

# Create caption
caption_text <- create_mm_caption(
  mm_year = current_year,
  mm_week = current_week,
  source_text = paste0("Our World in Data")
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    plot.title = element_text(
      size = rel(1.4), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_text(
      size = rel(0.9), hjust = 0, family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),

    # Legend formatting
    legend.position = "plot",
    legend.box.margin = margin(b = 10),
    legend.margin = margin(b = 5),
    legend.title = element_text(face = "bold"),

    # Axis formatting
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.ticks.length = unit(0.2, "cm"),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10)
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10)
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),
    axis.text.y = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),

    # Grid lines
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),
    panel.grid.minor = element_blank(),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code

```{r}
#| label: plot
#| warning: false

### |- P1  Income beeswarm plot----
beeswarm_plot <- ggplot(
  latest_data_income,
  aes(
    x = income_group, y = corruption_perceptions_index,
    color = income_group
  )
) +
  # Geoms
  geom_quasirandom(size = 2.5, alpha = 0.75, width = 0.35) +
  stat_summary(
    fun = median, geom = "crossbar", width = 0.6,
    color = "black", size = 1, alpha = 0.8
  ) +
  # Scales
  scale_color_manual(values = colors$palette, guide = "none") +
  scale_y_continuous(
    breaks = seq(0, 100, 20), limits = c(0, 100),
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  coord_flip(clip = "off") +
  # Labs
  labs(
    title = "Wealth strongly predicts clean governance",
    subtitle = str_glue(
      "2024 Corruption perceptions index by income level\n",
      "Each dot represents one country | Black bars show group medians"
    ),
    x = NULL,
    y = "Corruption Perceptions Index (0 = Most Corrupt, 100 = Least Corrupt)",
  ) +
  # Theme
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.major.x = element_line(color = "gray95", size = 0.25),
    axis.line.x = element_line(color = "gray30", size = 0.5),
  )
### |- P2  Dumbbell plot----
dumbbell_plot <- ggplot(biggest_movers, aes(y = entity_short)) +
  # Geoms
  geom_segment(aes(x = earliest_score, xend = latest_score),
               color = "gray60", size = 1.2, alpha = 0.8
  ) +
  geom_point(aes(x = earliest_score), color = "gray40", size = 3.5, alpha = 0.9) +
  geom_point(aes(x = latest_score, color = income_group), size = 3.5, alpha = 0.9) +
  geom_text(
    aes(
      x = pmax(earliest_score, latest_score) + 3,
      label = paste0(ifelse(trend > 0, "+", ""), round(trend))
    ),
    size = 3, fontface = "bold", color = "gray30", hjust = 0.1
  ) +
  # Scales
  scale_color_manual(values = colors$palette, guide = "none") +
  scale_x_continuous(
    breaks = seq(0, 100, 20), limits = c(0, 105),
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  # Labs
  labs(
    title = "Yet dramatic improvements are possible",
    subtitle = str_glue(
      "Largest changes in corruption perceptions (2012–2024)\n",
      "Gray dots: 2012 scores | Colored dots: 2024 scores (by income group)"
    ),
    x = "Corruption Perceptions Index",
    y = NULL,
  ) +
  # Theme
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank(),
    axis.line.x = element_line(color = "gray30", size = 0.5),
  )

### |-  combined plot ----
combined_plots <- beeswarm_plot | dumbbell_plot

combined_plots <- combined_plots +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.7),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_text(
        size = rel(1.1),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.9),
        lineheight = 0.9,
        margin = margin(t = 5, b = 0)
      ),
      plot.caption = element_markdown(
        size = rel(0.70),
        family = fonts$caption,
        color = colors$caption,
        hjust = 0.5,
        margin = margin(t = 10)
      )
    )
  )
```

7. Save

Show code

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 12.5, 
  height = 10
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1       patchwork_1.3.0  ggbeeswarm_0.7.2 glue_1.8.0      
 [5] scales_1.3.0     showtext_0.9-7   showtextdb_3.0   sysfonts_0.8.9  
 [9] ggtext_0.1.2     lubridate_1.9.3  forcats_1.0.0    stringr_1.5.1   
[13] dplyr_1.1.4      purrr_1.0.2      readr_2.1.5      tidyr_1.3.1     
[17] tibble_3.2.1     ggplot2_3.5.1    tidyverse_2.0.0  pacman_0.5.1    

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       beeswarm_0.4.0     xfun_0.49          htmlwidgets_1.6.4 
 [5] tzdb_0.5.0         yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0       
 [9] generics_0.1.3     curl_6.0.0         gifski_1.32.0-1    fansi_1.0.6       
[13] pkgconfig_2.0.3    ggplotify_0.1.2    skimr_2.1.5        readxl_1.4.3      
[17] lifecycle_1.0.4    compiler_4.4.0     farver_2.1.2       munsell_0.5.1     
[21] repr_1.1.7         janitor_2.2.0      codetools_0.2-20   snakecase_0.11.1  
[25] vipor_0.4.7        htmltools_0.5.8.1  yaml_2.3.10        pillar_1.9.0      
[29] camcorder_0.1.0    magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1  
[33] digest_0.6.37      stringi_1.8.4      rsvg_2.6.1         rprojroot_2.0.4   
[37] fastmap_1.2.0      grid_4.4.0         colorspace_2.1-1   cli_3.6.4         
[41] magrittr_2.0.3     base64enc_0.1-3    utf8_1.2.4         withr_3.0.2       
[45] timechange_0.3.0   rmarkdown_2.29     cellranger_1.1.0   hms_1.1.3         
[49] evaluate_1.0.1     knitr_1.49         markdown_1.13      gridGraphics_0.5-1
[53] rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6        
[57] renv_1.0.3         svglite_2.1.3      rstudioapi_0.17.1  jsonlite_1.8.9    
[61] R6_2.5.1           fs_1.6.5           systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2025_32.qmd.

For the full repository, click here.

10. References

Expand for References

Data:

Makeover Monday 2025 Week 32: Corruption Perceptions Index (CPI)

Article

Corruption

--- title: "The economics of corruption perceptions: structure vs change, 2012-2024" subtitle: "How economic development shapes corruption perceptions, yet dramatic improvements remain possible" description: "A comprehensive analysis of global corruption perceptions revealing how economic development strongly predicts governance quality, while individual countries can still achieve dramatic improvements. Using World Bank income classifications and 13 years of Transparency International data, this Makeover Monday project transforms a static table into an engaging dual-chart story showing both structural patterns and exceptional cases of anti-corruption progress across 180+ countries." author: "Steven Ponce" date: "2025-08-05" categories: ["MakeoverMonday", "Data Visualization", "R Programming", "2025"] tags: [ "corruption-perceptions-index", "transparency-international", "world-bank-income-groups", "governance-analysis", "economic-development", "beeswarm-plot", "dumbbell-chart", "ggplot2", "data-transformation", "comparative-analysis", "global-governance", "anti-corruption", "policy-insights" ] image: "thumbnails/mm_2025_32.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" self-contained: true theme: light: [flatly, assets/styling/custom_styles.scss] dark: [darkly, assets/styling/custom_styles_dark.scss] editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true --- ```{r} #| label: setup-links #| include: false # CENTRALIZED LINK MANAGEMENT ## Project-specific info current_year <- 2025 current_week <- 32 project_file <- "mm_2025_32.qmd" project_image <- "mm_2025_32.png" ## Data Sources data_main <- "https://data.world/makeovermonday/corruption" data_secondary <- "https://ourworldindata.org/grapher/ti-corruption-perception-index?tab=table&time=earliest..2024&country=~NZL" ## Repository Links repo_main <- "https://github.com/poncest/personal-website/" repo_file <- paste0("https://github.com/poncest/personal-website/blob/master/data_visualizations/MakeoverMonday/", current_year, "/", project_file) ## External Resources/Images chart_original <- "https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2025/Week_32/original_chart.png" ## Organization/Platform Links org_primary <- "https://ourworldindata.org/grapher/ti-corruption-perception-index?tab=table&time=earliest..2024&country=~NZL" org_secondary <- "https://ourworldindata.org/" # Helper function to create markdown links create_link <- function(text, url) { paste0("[", text, "](", url, ")") } # Helper function for citation-style links create_citation_link <- function(text, url, title = NULL) { if (is.null(title)) { paste0("[", text, "](", url, ")") } else { paste0("[", text, "](", url, ' "', title, '")') } } ``` ### Original The original visualization **Corruption Perceptions Index (CPI)** comes from `r create_link("Our orld in Data", data_secondary)`  ![Original visualization](https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2025/Week_32/original_chart.png) ### Makeover ![Two-panel chart showing corruption perceptions by income level. Left panel: beeswarm plot reveals higher-income countries (orange dots) cluster at better corruption scores (60-90), while lower-income countries (blue dots) spread across worse scores (10-60). Right panel: The dumbbell chart shows the largest changes from 2012 to 2024, with Seychelles leading at a +20-point improvement and Estonia at a +12-point improvement, demonstrating that countries across all income levels can achieve dramatic anti-corruption progress.](`r project_image`){#fig-1} ### <mark> **Steps to Create this Graphic** </mark> #### 1. Load Packages & Setup ```{r} #| label: load #| warning: false #| message: false #| results: "hide" ## 1. LOAD PACKAGES & SETUP ---- suppressPackageStartupMessages({ if (!require("pacman")) install.packages("pacman") pacman::p_load( tidyverse, # Easily Install and Load the 'Tidyverse' ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs scales, # Scale Functions for Visualization glue, # Interpreted String Literals ggbeeswarm, # Categorical Scatter (Violin Point) Plots patchwork # The Composer of Plots ) }) ### |- figure size ---- camcorder::gg_record( dir = here::here("temp_plots"), device = "png", width = 12, height = 10, units = "in", dpi = 320 ) # Source utility functions suppressMessages(source(here::here("R/utils/fonts.R"))) source(here::here("R/utils/social_icons.R")) source(here::here("R/utils/image_utils.R")) source(here::here("R/themes/base_theme.R")) ``` #### 2. Read in the Data ```{r} #| label: read #| include: true #| eval: true #| warning: false #| cpi_raw <- readxl::read_excel( here::here('data/MakeoverMonday/2025/Corruption Perception Index.xlsx')) |> janitor::clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(cpi_raw) skimr::skim(cpi_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false # Data cleaning and preparation cpi_clean <- cpi_raw |> filter(!is.na(corruption_perceptions_index)) |> filter(year >= 2012, year <= 2024) # Helper function to assign World Bank income groups (FY2024 classification) create_income_groups <- function(data) { data |> mutate( income_group = case_when( # ---- High Income Countries (based on WB FY2024) ---- code %in% c( # Western Europe & North America "DNK", "NOR", "SWE", "CHE", "SGP", "NZL", "FIN", "DEU", "NLD", "LUX", "AUT", "AUS", "CAN", "USA", "GBR", "BEL", "JPN", "FRA", "IRL", "ISL", "ESP", "ITA", "KOR", "ISR", # Eastern Europe (High Income) "SVN", "CZE", "EST", "SVK", "PRT", "POL", "LTU", "LVA", "HUN", "HRV", "GRC", "CYP", "MLT", # Other High Income "URY", "CHL", "PAN", "ARE", "QAT", "KWT", "BHR", "OMN", "SAU", "BRB", "TTO" ) ~ "High\nIncome", # ---- Upper Middle Income ---- code %in% c( # Latin America "ARG", "MYS", "THA", "MEX", "CRI", "BRA", "COL", "PER", "ECU", "DOM", # Europe & Central Asia "ROU", "BGR", "TUR", "MNE", "SRB", "MKD", "ALB", "BIH", "ARM", "GEO", "AZE", "KAZ", "BLR", # Other Upper Middle "CHN", "RUS", "ZAF", "BWA", "NAM", "JAM", "LBN", "JOR", "TUN", "DZA", "IRQ", "IRN", "LBY", "GAB" ) ~ "Upper Middle\nIncome", # ---- Lower Middle Income ---- code %in% c( # South & East Asia "IND", "IDN", "PHL", "VNM", "BGD", "PAK", "LKA", "NPL", "KHM", "LAO", "MMR", "MNG", # Europe & Central Asia "UZB", "KGZ", "TJK", "UKR", "MDA", # Middle East & North Africa "MAR", "EGY", "PSE", "SYR", "YEM", "SDN", "DJI", # Latin America "BOL", "PRY", "GTM", "HND", "NIC", "SLV", "GUY", "SUR", # Sub-Saharan Africa "KEN", "TZA", "UGA", "RWA", "ZMB", "AGO", "CIV", "GHA", "CMR", "SEN", "NGA", "BEN" ) ~ "Lower Middle\nIncome", # ---- Default: Low Income ---- TRUE ~ "Low\nIncome" ), # Create ordered factor for logical display income_group = factor( income_group, levels = c("Low\nIncome", "Lower Middle\nIncome", "Upper Middle\nIncome", "High\nIncome"), ordered = TRUE ) ) } # Apply income classification cpi_with_income <- cpi_clean |> create_income_groups() # Calculate country trends for dumbbell chart country_trends <- cpi_with_income |> group_by(entity) |> filter(any(year == 2012) & any(year == 2024)) |> summarise( trend = corruption_perceptions_index[year == 2024][1] - corruption_perceptions_index[year == 2012][1], latest_score = corruption_perceptions_index[year == 2024][1], earliest_score = corruption_perceptions_index[year == 2012][1], income_group = first(income_group), .groups = "drop" ) |> filter(!is.na(trend), !is.na(latest_score), !is.na(earliest_score)) # Beeswarm plot data latest_data_income <- cpi_with_income |> filter(year == 2024) |> arrange(desc(corruption_perceptions_index)) # Dumbbell plot data biggest_movers <- country_trends |> filter(abs(trend) >= 5) |> arrange(desc(trend)) |> slice_head(n = 20) |> mutate( income_abbrev = case_when( income_group == "High\nIncome" ~ "HI", income_group == "Upper Middle\nIncome" ~ "UMI", income_group == "Lower Middle\nIncome" ~ "LMI", income_group == "Low\nIncome" ~ "LI", TRUE ~ "Unknown" ), entity_with_income = glue("{entity} ({income_abbrev})"), entity_short = if_else( nchar(entity_with_income) > 25, glue("{str_sub(entity, 1, 18)}... ({income_abbrev})"), entity_with_income ), entity_short = fct_reorder(entity_short, latest_score) ) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors(palette = list( "Low\nIncome" = "#2166AC", "Lower Middle\nIncome" = "#5AAE61", "Upper Middle\nIncome" = "#9970AB", "High\nIncome" = "#E08214" )) ### |- titles and caption ---- title_text <- str_glue("The economics of corruption perceptions: structure vs change, 2012-2024") subtitle_text <- str_glue( "How economic development shapes corruption perceptions, yet dramatic improvements remain possible" ) # Create caption caption_text <- create_mm_caption( mm_year = current_year, mm_week = current_week, source_text = paste0("Our World in Data") ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( plot.title = element_text( size = rel(1.4), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, hjust = 0, margin = margin(t = 5, b = 10) ), plot.subtitle = element_text( size = rel(0.9), hjust = 0, family = fonts$subtitle, color = alpha(colors$subtitle, 0.9), lineheight = 1.1, margin = margin(t = 0, b = 20) ), # Legend formatting legend.position = "plot", legend.box.margin = margin(b = 10), legend.margin = margin(b = 5), legend.title = element_text(face = "bold"), # Axis formatting axis.ticks.y = element_blank(), axis.ticks.x = element_line(color = "gray", linewidth = 0.5), axis.ticks.length = unit(0.2, "cm"), axis.title.x = element_text( face = "bold", size = rel(0.85), margin = margin(t = 10) ), axis.title.y = element_text( face = "bold", size = rel(0.85), margin = margin(r = 10) ), axis.text.x = element_text( size = rel(0.85), family = fonts$subtitle, color = colors$text ), axis.text.y = element_text( size = rel(0.85), family = fonts$subtitle, color = colors$text ), # Grid lines panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4), panel.grid.minor = element_blank(), # Margin plot.margin = margin(20, 20, 20, 20) ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- P1 Income beeswarm plot---- beeswarm_plot <- ggplot( latest_data_income, aes( x = income_group, y = corruption_perceptions_index, color = income_group ) ) + # Geoms geom_quasirandom(size = 2.5, alpha = 0.75, width = 0.35) + stat_summary( fun = median, geom = "crossbar", width = 0.6, color = "black", size = 1, alpha = 0.8 ) + # Scales scale_color_manual(values = colors$palette, guide = "none") + scale_y_continuous( breaks = seq(0, 100, 20), limits = c(0, 100), expand = expansion(mult = c(0.02, 0.02)) ) + coord_flip(clip = "off") + # Labs labs( title = "Wealth strongly predicts clean governance", subtitle = str_glue( "2024 Corruption perceptions index by income level\n", "Each dot represents one country | Black bars show group medians" ), x = NULL, y = "Corruption Perceptions Index (0 = Most Corrupt, 100 = Least Corrupt)", ) + # Theme theme( panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(), panel.grid.major.x = element_line(color = "gray95", size = 0.25), axis.line.x = element_line(color = "gray30", size = 0.5), ) ### |- P2 Dumbbell plot---- dumbbell_plot <- ggplot(biggest_movers, aes(y = entity_short)) + # Geoms geom_segment(aes(x = earliest_score, xend = latest_score), color = "gray60", size = 1.2, alpha = 0.8 ) + geom_point(aes(x = earliest_score), color = "gray40", size = 3.5, alpha = 0.9) + geom_point(aes(x = latest_score, color = income_group), size = 3.5, alpha = 0.9) + geom_text( aes( x = pmax(earliest_score, latest_score) + 3, label = paste0(ifelse(trend > 0, "+", ""), round(trend)) ), size = 3, fontface = "bold", color = "gray30", hjust = 0.1 ) + # Scales scale_color_manual(values = colors$palette, guide = "none") + scale_x_continuous( breaks = seq(0, 100, 20), limits = c(0, 105), expand = expansion(mult = c(0.02, 0.02)) ) + # Labs labs( title = "Yet dramatic improvements are possible", subtitle = str_glue( "Largest changes in corruption perceptions (2012–2024)\n", "Gray dots: 2012 scores | Colored dots: 2024 scores (by income group)" ), x = "Corruption Perceptions Index", y = NULL, ) + # Theme theme( panel.grid.minor = element_blank(), panel.grid.major = element_blank(), axis.line.x = element_line(color = "gray30", size = 0.5), ) ### |- combined plot ---- combined_plots <- beeswarm_plot | dumbbell_plot combined_plots <- combined_plots + plot_annotation( title = title_text, subtitle = subtitle_text, caption = caption_text, theme = theme( plot.title = element_text( size = rel(1.7), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(1.1), family = fonts$subtitle, color = alpha(colors$subtitle, 0.9), lineheight = 0.9, margin = margin(t = 5, b = 0) ), plot.caption = element_markdown( size = rel(0.70), family = fonts$caption, color = colors$caption, hjust = 0.5, margin = margin(t = 10) ) ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot_patchwork( plot = combined_plots, type = "makeovermonday", year = current_year, week = current_week, width = 12.5, height = 10 ) ``` #### 8. Session Info ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in `r create_link(project_file, repo_file)`. For the full repository, `r create_link("click here", repo_main)`. ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data: - Makeover Monday `r current_year` Week `r current_week`: `r create_link("Corruption Perceptions Index (CPI)", data_main)` 2. Article - `r create_link("Corruption", data_secondary)` :::